Autism spectrum disorder (ASD) is a genetically and phenotypically heterogeneous disorder. Efforts to identifying subtypes based on the behavioral and neural phenotypes have been unsuccessful, and the rarity of specific genetic events renders subtyping at the CNV or exonic mutation level challenging and exceedingly resource-intensive. However, exploring the impact of gene disruptions in common pathways holds promise for elucidating phenotypic subtypes and clarifying genotype-phenotype relations in ASD. Most importantly, the identification of pathway-based subtypes will advance translational goals of selecting interventions, defining treatment targets, and predicting outcomes. The current project aims to investigate the behavioral and neural phenotype, obtained through electroencephalography (EEG), in pre-identified individuals with ASD with gene mutations in the beta-catenin pathway. This project addresses the critical need to understand the pathogenesis of ASD by elucidating the relationship between ASD risk genes, brain function and behavior and the need to address the wide heterogeneity in ASD by careful subtype identification. We will include 60 participants with ASD with gene mutations in the beta-catenin pathway, 60 participants with ASD with gene disruptions falling in other pathways, and 60 participants with ASD without any identified CNVs or gene disrupting mutations along with 30 typical individuals. Given the role of beta-catenin in neuronal adhesion, synapse formation, and in the transcription of target genes linked to cell growth, differentiation, and migration, as well as preliminary evidence of behavioral disruptions in learning and memory, evidence of dysfunction of beta-catenin may be observed at both the behavioral and neurophysiological level. Electrophysiological measures will allow us to specifically address the brain systems related to learning and memory as well as those related to the core features of autism. This research holds promise for discovery of gene-brain-behavior relationships, attained through integration of genotypic, EEG, and behavioral data.